Ankit Chaplot, International Journal of Advanced Trends in Computer Applications (IJATCA) Volume 6, Number 1, March - 2019, pp. 5-10
ISSN: 2395-3519
International Journal of Advanced Trends in Computer Applications www.ijatca.com
BUSINESS SALES ANALYSIS AND CUSTOMER RETENTION 1
Ankit Chaplot, 2Ayush Tiwari, 3Gaurav Bisaria, 4Mustafa Raj 1,2,3,4
Savitribai Phule Pune University ankitchaplot1@gmail.com, 2ayushtiwari811@gmail.com, 3gbisaria7@gmail.com, 4 mustafaraj9715@gmail.com
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Abstract: This project proposes a method that predicts customer value by focusing on purchasing behaviour. The method generates a relevance model for purchase days and amount in each period between customer value and purchasing histories beforehand based on a consumer panel survey. We have adopted the random forest method to generate the prediction model. The proposed method facilitates the provisioning of smart customer management to each customer according to level such as suggesting products or services. The problem faced by the company is how to determine potential customers and apply CRM (Customer Relationship Management) in order to perform the right marketing strategy, so it can bring benefits to the company. This research aims to perform clustering and profiling customer by using the model of Recency Frequency and Monetary (RFM) to provide customer relationship management (CRM). The method used in this study consists of four steps: data mining from transaction history data of customer sales, data mining modeling using RFM and customer classification with decision tree, determination of customer loyalty level and recommendation of customer relationship management (CRM).
Keywords: Transaction Data Mining; RFM; Clustering; Kmeans; Decision Tree; Profiling Customers; CRM.
I. Introduction Most companies use a customer relationship management system to improve their relation attributes such as demographic data, preference, purchase history, usage history and contact history. By using and analyzing these data, it is possible to respond to customer. Finding excellent customers, which means those who visit and purchase frequently is important for improving sales and the efficiency of various measures taken to target customers. The project defines the metric of customer level in this paper. Specifically, customer level can be used for customer selection such as optimizing the approach to customers, campaigns to train best customer candidates and measures to activate dormant customers. By holding and using the customer level in a customer relationship management system, we can build a more productive relationship with customers. RFM is existing approach to calculate the customer level. RFM is one of several methods that can extract customer level from purchase history data. From the purchase history, this method extracts as indicators
Recency (last purchase date), Frequency (purchase frequency), Monetary (purchase price). They can be used to indicate customer’s level. However, there are many customers whose purchase history data is too scant to allow customer level determination. If we can predict future investment, human resources and expertise, to the company’s value chain. Enhanced performance can result from improved communication and coordination with this set of suppliers. With fewer vendors, increased with customers. Customer level from a small amount of purchase history, we can better support existing customers and acquire new customers as excellent customers. We propose that predicting customer level with the least possible delay is also important for customer relationship management. Nevertheless, no proposal has clarified the effectiveness of the amount and types of purchase data used to predict customer. Such systems record and manage customer level. The project is organized as follows. Section I discusses related work and existing techniques for customer level determination. Section II introduces the customer relationship management system. Purchase data used to
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